Radiologic–pathologic correlation of major versus minor extranodal extension in oral cavity cancer
Bibliographic record
Abstract
BACKGROUND: To evaluate the diagnostic performance of radiologic extranodal extension (rENE) in predicting major (>2 mm) and minor (≤2 mm) pathologic ENE (pENE). METHODS: All oral cavity squamous cell carcinoma patients who underwent neck dissection with pathological nodal disease (pN+) between 2010 and 2015 were reviewed. Preoperative computed tomography and/or magnetic resonance imaging were reviewed by two head and neck neuroradiologists. RESULTS: Three hundred and thirty-four patients were included. The sensitivity and specificity of rENE were 37% [95% CI 29-44] and 98% [95% CI 96-100], respectively. Sensitivity for pENE improved in the subset of patients with major ENE (48% [95% CI 38-57]). The presence of rENE was associated with inferior 3-year overall survival: 26% [95% CI 17-41] versus 60% [95% CI 54-67]. CONCLUSIONS: This large cohort study demonstrates high specificity, but low sensitivity for preoperative imaging in the detection of pENE in OCSCC. Patients with rENE demonstrated poor OS. pENE in the absence of rENE is still an adverse risk factor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".